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Application Programming Interface Design and Scalability Questions

Designing application programming interfaces that remain reliable, performant, and maintainable at high scale. Candidates should understand how interface decisions affect scalability, availability, latency, and operational complexity and be able to reason about trade offs between client complexity and server responsibility. Core areas include stateless interface design, pagination and cursor strategies for large result sets, filtering and search optimization, payload minimization, batching and streaming, and techniques to reduce server load while preserving client experience. Resilience and operational controls include rate limiting and quota management, throttling, backpressure and flow control, retry semantics and idempotency patterns, error format design and explicit identification of retryable errors, and strategies for graceful degradation under overload. Evolution and compatibility topics include backward compatible versioning strategies, deprecation policies, contract design and testing approaches to avoid breaking consumers. Infrastructure and deployment considerations include API gateway and edge patterns, interaction with load balancers and traffic distribution, caching and content delivery, routing fault tolerance, health checks and canary rollout strategies, and observability through metrics, distributed tracing, and logging to support capacity planning and incident response. Security considerations such as scalable authentication and authorization, credential and key management, and permission models are also important. Candidates should be prepared to discuss concrete patterns, trade offs, algorithms, and operational playbooks for designing and running high traffic application programming interfaces.

MediumTechnical
0 practiced
Design an API error schema that explicitly marks whether an error is retryable by the client. Include fields for machine-readable error code, human message, retry-after metadata, suggested backoff strategy, and documentation link. Provide example payloads for a transient DB lock and an authentication failure, explaining how clients should react.
MediumTechnical
0 practiced
As a solutions architect, list the key metrics, traces, and logs you would instrument for a public API to support capacity planning and incident response. Include dimensionality examples (by endpoint, tenant, region), latency percentiles, error budgets, sampled distributed traces, and log correlation keys. Explain a tagging strategy and trade-offs with metric cardinality.
HardSystem Design
0 practiced
Design a canary rollout system for API changes with automated analysis and rollback triggers. Include traffic routing mechanics, golden metrics and statistical tests for regressions, progressive traffic shifting, observation windows, auto-rollback criteria, and coordination across dependent services and regions.
HardSystem Design
0 practiced
Architect a public read-heavy API platform that serves 100k requests per second, supports 100M users, and must meet a 50ms P99 latency target for read endpoints. Provide a high-level architecture including API gateways, load balancing, edge caches, read-replica patterns, sharding approaches, denormalization strategies, autoscaling, and observability needed to operate at this scale. State key capacity assumptions and trade-offs.
MediumSystem Design
0 practiced
Design a webhook delivery system that reliably delivers events to third-party endpoints at scale. Address durable queuing, retry semantics with exponential backoff, deduplication, endpoint verification, per-customer rate limits, dead-letter handling, and the developer experience for monitoring delivery status and failures.

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